How to build scalable, differentiable multi-agent foraging simulations?
Foragax: An Agent Based Modelling framework based on JAX
September 11, 2024
https://arxiv.org/pdf/2409.06345This paper introduces Foragax, a Python toolkit built on Google's JAX library for simulating large-scale multi-agent foraging behaviors. Foragax allows researchers to model agents with custom behaviors and policies, making it relevant to LLM-based multi-agent systems.
Key takeaways for LLM systems:
- Scalability: Simulates thousands of agents concurrently thanks to JAX's parallel processing, surpassing typical limitations.
- Customizable: Model agents with diverse policies, which could be driven by LLMs to enable complex decision-making.
- Open-ended tasks: Focuses on continuous, non-episodic simulations, suitable for studying emergent behavior in LLM-agents over time.
- Potential for LLM integration: While current examples use simpler neural networks, Foragax's structure allows for plugging in LLM-based policies for agents, opening up research opportunities in complex, long-term multi-agent interactions.